Practical Machine Learning

Practical Machine Learning

Practical Machine Learning

Johns Hopkins University

About this course: One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

This week we introduce a number of machine learning algorithms you can use to complete your course project.

5 videos

Video: Predicting with trees

Video: Bagging

Video: Random Forests

Video: Boosting

Video: Model Based Prediction

Graded: Quiz 3

WEEK 4

Week 4: Regularized Regression and Combining Predictors

This week, we will cover regularized regression and combining predictors.

4 videos, 2 readings

Video: Regularized regression

Video: Combining predictors

Video: Forecasting

Video: Unsupervised Prediction

Reading: Course Project Instructions (READ FIRST)

Reading: Post-Course Survey

Graded: Quiz 4

Graded: Prediction Assignment Writeup

Graded: Course Project Prediction Quiz

FAQs

How It Works

Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

Help from Your Peers

Connect with thousands of other learners and debate ideas, discuss course material,
and get help mastering concepts.

Certificates

Earn official recognition for your work, and share your success with friends,
colleagues, and employers.

Creators

Johns Hopkins University

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

Pricing

Audit

Purchase Course

Access to course materials

Available

Available

Access to graded materials

-

Not available

Available

Receive a final grade

-

Not available

Available

Earn a shareable Course Certificate

-

Not available

Available

Ratings and Reviews

Rated 4.4 out of 5 of 1,375 ratings

RK

A great introduction to machine learning and it does a good job building on the material from the previous classes.

Very interesting course

Some problems with current and old versions of packages and problems with using other packages on different operating systems. Though that did also help foster an independent research style which will help me in the future.

LG

A very good starter course on Machine Learning in R with great links to various resources that students and delve deeper into the various topics.